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Data Studio allows you to communicate data simply and in a repeatable format, and their expanded integrations, customizations, and editability have made Data Studio dashboards extremely powerful.

A relatively new feature, data blending, came out last year. This underused function can do a lot of cool things; it also has some limitations. Once you’ve got your head around the basics, the possibilities are endless.

What is data blending?

Data blending in Google Data Studio lets you create charts based on multiple data sources. Separate data sources—not just those from the same application—can be combined as long as they’re comparable (i.e. share a “join key,” something discussed more below).

Traditionally, if you wanted to create direct comparisons from different sources, you had to export data from each source and combine them in Excel. If you suddenly needed to study a longer timeframe, you had to download the data again and start over.

By default, each element in Data Studio pulls information from a single data source. You could hook up multiple data sources to a dashboard, but until the introduction of data blending, you couldn’t present those together in a single chart or table.

With a few clicks, data blending can reveal valuable relationships between data sets. Because everything happens within Data Studio, you save time on data manipulation and enjoy new opportunities to present findings.

Jon Meck, Senior Marketing Director at Bounteous, highlighted several benefits his team has identified:

Jon Meck:

With a familiar and intuitive interface, we continue to find use cases for data blending that showcase the connected Google ecosystem, facilitate real-time decision-making, and save hours of manual work.

We love using it to combine third-party data around advertising details or CRM data, and it’s allowed us to bring in personal data from external sources that are off-limits for other Google products.

Keys are…key…to data blending

To blend data, the data sources need to share a common dimension. This is known as a “join key.” It’s the common denominator to compare data. Your join key could be a page URL, product name, user ID, or many other things.

The simplest join key is “Date.” Measuring things over time is a common part of data analysis, so let’s use that as an example.

Selecting “Date”as the join key lets you spot correlations in data sets. Want to see how many leads came into your CRM against compared to organic sessions last month? No problem:

Choosing the right key depends on what you’re trying to illustrate. A good starting point is to come up with a hypothesis. For example, your hypothesis might be that “website users are more likely to pay via PayPal if they’re on a mobile device.”